Research And Implementation Of High-speed Sewing Machine Image Tracking System

The trajectory data of industrial sewing machines are usually used to give a data-based comparative analysis of the differences between actual moving behavior and designed moving behavior, and provide a guide to the research and development work of the sewing product, but the data are difficult to observe and record by artificial means. Image processing method provides a new way for it:obtain the trajectory data by tracking spare parts in the sewing video images. Target tracking is a key factor in this process, and plays a decisive role in analyzing problems and drawing the right conclusion, so the study of target tracking method applicable to high-speed sewing machine motion images has important application value.In this paper, with the motion image sequence of industrial sewing machines captured by high-speed camera as the research object, High-speed Sewing Machine Image Tracking System is designed and implemented, the problem of the poor tracking stability under different movement pattern in the original system is solved, and a steady tracking is achieved for sewing machine spare parts. First of all, the basic theory of correlation tracking algorithm is introduced. In view of the analysis of the image sequence' characteristics, the consideration for user requirements and the comparison and simulation of four kinds of gray-scale image correlation tracking algorithms, the average squared difference correlation tracking algorithm is used to complete the tracking of spare parts and achieves a short-term stabilization tracking. Then, based on the introduction of the particle filter theory, embed the correlation tracking algorithm into the tracking framework of particle filter, use particle propagation and observation to replace the matching and searching process of the traditional correlation algorithm, and achieve long-term stability of the sewing machine spare parts tracking. Last, the method that it smoothes particles after random sampling is introduced for re-sampling and the smoothing way are improved. This re-sampling way improves the diversity of particles and enhances the stability of tracking with breeding power values particles. And after a large number of experiments the total number of particles is determined, effectively reducing the tracking time on the basis of maintaining tracking accuracy.Experimental results show that with the above method, the stability of tracking spare parts is greatly improved. More accurate trajectories of components are obtained than that of the original system and reliable analytical data are provided for the study of the sewing machine designing.